Development of a near-infrared spectroscopy method (NIRS) for fast analysis of total, indolic, aliphatic and individual glucosinolates in new bred open pollinating genotypes of broccoli ( Brassica oleracea convar. botrytis var. italica )

Progresses and Prospects on Glucosinolate Detection in Cruciferous Plants

This review provides a comprehensive summary of the latest international research on detection methods for glucosinolates in cruciferous plants. This article examines various analytical techniques, including high-performance liquid chromatography (HPLC), liquid chromatography-mass spectrometry (LC-MS), enzyme-linked immunosorbent assay (ELISA), and capillary electrophoresis (CE), while highlighting their respective advantages and limitations. Additionally, this review delves into recent advancements in sample preparation, extraction, and quantification methods, offering valuable insights into the accurate and efficient determination of glucosinolate content across diverse plant materials. Furthermore, it underscores the critical importance of the standardization and validation of these methodologies to ensure reliable glucosinolate analyses in both scientific research and industrial applications.

Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review

Fruits and vegetables are an important part of our daily diet and contain low-content components that are crucial for our health. Detecting these components accurately is of paramount significance. However, traditional detection methods face challenges such as complex sample processing, slow detection speed, and the need for highly skilled operators. These limitations fail to meet the growing demand for intelligent and rapid detection of low-content components in fruits and vegetables. In recent years, significant progress has been made in intelligent rapid detection technology, particularly in detecting high-content components in fruits and vegetables. However, the accurate detection of low-content components remains a challenge and has gained considerable attention in current research. This review paper aims to explore and analyze several intelligent rapid detection techniques that have been extensively studied for this purpose. These techniques include near-infrared spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy, and terahertz spectroscopy, among others. This paper provides detailed reports and analyses of the application of these methods in detecting low-content components. Furthermore, it offers a prospective exploration of their future development in this field. The goal is to contribute to the enhancement and widespread adoption of technology for detecting low-content components in fruits and vegetables. It is expected that this review will serve as a valuable reference for researchers and practitioners in this area.

Determination of glucosinolates and isothiocyanates in glucosinolate-rich vegetables and oilseeds using infrared spectroscopy: A systematic review

Cruciferous vegetables and oilseeds are rich in glucosinolates that can transform into isothiocyanates upon enzymic hydrolysis during post-harvest handling, food preparation and/or digestion. Vegetables contain glucosinolates that have beneficial bioactivities, while glucosinolates in oilseeds might have anti-nutritional properties. It is therefore important to monitor and assess glucosinolates and isothiocyanates content through the food value chain as well as for optimized crop production. Vibrational spectroscopy methods, such as infrared (IR) spectroscopy, are used as a nondestructive, rapid and low-cost alternative to the current and common costly, destructive, and time-consuming techniques. This systematic review discusses and evaluates the recent literature available on the use of IR spectroscopy to determine glucosinolates and isothiocyanates in vegetables and oilseeds. NIR spectroscopy was used to predict glucosinolates in broccoli, kale, rocket, cabbage, Brussels sprouts, brown mustard, rapeseed, pennycress, and a combination of Brassicaceae family seeds. Only one study reported the use of NIR spectroscopy to predict broccoli isothiocyanates. The major limitations of these studies were the absence of the critical evaluation of errors associated with the reference method used to develop the calibration models and the lack of interpretation of loadings or regression coefficients used to predict glucosinolates.

Estimation of Glucosinolates and Anthocyanins in Kale Leaves Grown in a Plant Factory Using Spectral Reflectance

The spectral reflectance technique for the quantification of the functional components was applied in different studies for different crops, but related research on kale leaves is limited. This study was conducted to estimate the glucosinolate and anthocyanin components of kale leaves cultivated in a plant factory based on diffuse reflectance spectroscopy through regression methods. Kale was grown in a plant factory under different treatments. After specific periods of transplantation, leaf samples were collected, and reflectance spectra were measured immediately from nine different points on each leaf. The same leaf samples were freeze-dried and stored for analysis of the functional components. Regression procedures, such as principal component regression (PCR), partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR), were applied to relate the functional components with the spectral data. In the laboratory analysis, progoitrin and glucobrassicin, as well as cyanidin and malvidin, were found to be dominating components in glucosinolates and anthocyanins, respectively. From the overall analysis, the SMLR model showed better performance, and the identified wavelengths for estimating the glucosinolates and anthocyanins were in the early near-infrared (NIR) region. Specifically, reflectance at 742, 761, 787, 796, 805, 833, 855, 932, 947, and 1000 nm showed a strong correlation.

A comprehensive Vis-NIRS equation for rapid quantification of seed glucosinolate content and composition across diverse Brassica oilseed chemotypes

The globally cultivated Brassica crops contain high deliverable concentrations of health-promoting glucosinolates. Development of a Visible-Near InfraRed Spectroscopy (Vis-NIRS) calibration to profile different glucosinolate components from 641 diverse Brassica juncea chemotypes was attempted in this study. Principal component analysis of HPLC-determined glucosinolates established the distinctiveness of four B. juncea populations used. Subsequently, modified partial least square regression based population-specific and combined Vis-NIRS models were developed, wherein the combined model exhibited higher coefficient of determination (R²; 0.81-0.97) for eight glucosinolates and higher ratio of prediction determination (RPD; 2.42-5.35) for seven glucosinolates in B. juncea populations. Furthermore, range error ratio (RER > 4) for twelve and RER > 10 for eight glucosinolates make the combined model acceptable for screening and quality control. The model also provided excellent prediction for aliphatic glucosinolates in four oilseed Brassica species. Overall, our work highlights the potential of Vis-NIR spectroscopy in estimating glucosinolate content in the economically important Brassica oilseeds.

Pre- and Post-harvest Factors Affecting Glucosinolate Content in Broccoli

Owing to several presumed health-promoting biological activities, increased attention is being given to natural plant chemicals, especially those frequently entering the human diet. Glucosinolates (GLs) are the main bioactive compounds found in broccoli (Brassica oleracea L. var. italica Plenck). Their regular dietary assumption has been correlated with reduced risk of various types of neoplasms (lung, colon, pancreatic, breast, bladder, and prostate cancers), some degenerative diseases, such as Alzheimer's, and decreased incidence of cardiovascular pathologies. GL's synthesis pathway and regulation mechanism have been elucidated mainly in Arabidopsis. However, nearly 56 putative genes have been identified as involved in the B. oleracea GL pathway. It is widely recognized that there are several pre-harvest (genotype, growing environment, cultural practices, ripening stage, etc.) and post-harvest (harvesting, post-harvest treatments, packaging, storage, etc.) factors that affect GL synthesis, profiles, and levels in broccoli. Understanding how these factors act and interact in driving GL accumulation in the edible parts is essential for developing new broccoli cultivars with improved health-promoting bioactivity. In this regard, any systematic and comprehensive review outlining the effects of pre- and post-harvest factors on the accumulation of GLs in broccoli is not yet available. Thus, the goal of this paper is to fill this gap by giving a synoptic overview of the most relevant and recent literature. The existence of substantial cultivar-to-cultivar variation in GL content in response to pre-harvest factors and post-harvest manipulations has been highlighted and discussed. The paper also stresses the need for adapting particular pre- and post-harvest procedures for each particular genotype in order to maintain nutritious, fresh-like quality throughout the broccoli value chain.

The glucosinolates and their bioactive derivatives in Brassica: a review on classification, biosynthesis and content in plant tissues, fate during and after processing, effect on the human organism and interaction with the gut microbiota

The present study is a systematic review of the scientific literature reporting content, composition and biosynthesis of glucosinolates (GLS), and their derivative compounds in Brassica family. An amended classification of brassica species, varieties and their GLS content, organized for the different plant organs and in uniformed concentration measure unit, is here reported for the first time in a harmonized and comparative manner. In the last years, the studies carried out on the effect of processing on vegetables and the potential benefits for human health has increased rapidly and consistently the knowledge on the topic. Therefore, there was the need for an updated revision of the scientific literature of pre- and post-harvest modifications of GLS content, along with the role of gut microbiota in influencing their bioavailability once they are ingested. After analyzing and standardizing over 100 articles and the related data, the highest GLS content in Brassica, was declared in B. nigra (L.) W. D. J. Koch (201.95 ± 53.36 µmol g^-1), followed by B. oleracea Alboglabra group (180.9 ± 70.3 µmol g^-1). The authors also conclude that food processing can influence significantly the final content of GLS, considering the most popular methods: boiling, blanching, steaming, the latter can be considered as the most favorable to preserve highest level of GLS and their deriviatives. Therefore, a mild-processing strategic approach for GLS or their derivatives in food is recommended, in order to minimize the loss of actual bioactive impact. Finally, the human gut microbiota is influenced by Brassica-rich diet and can contribute in certain conditions to the increasing of GLS bioavailability but further studies are needed to assess the actual role of microbiomes in the bioavailability of healthy glucosinolate derivatives.

Evaluation of the analytical performances of two Raman handheld spectrophotometers for pharmaceutical solid dosage form quantitation

This paper addresses the issue of pharmaceutical solid dosage form quantitation using handheld Raman spectrophotometers. The two spectrophotometers used are designed with different technologies: one allows getting a more representative sampling with the Orbital Raster Scanning technology and the other one allows setting acquisition parameters. The goal was to evaluate which technology could provide the best analytical results. Several parameters were optimized to get the lowest prediction error in the end. The main objective of this study was to evaluate if this kind of instrument would be able to identify substandard medicines. For that purpose, two case-study were explored. At first, a full ICH Q2 (R1) compliant validation was performed for moderate Raman scatterer active pharmaceutical ingredient (API) in a specific formulation. It was successfully validated in the ±15% relative total error acceptance limits, with a RMSEP of 0.85% (w/w). Subsequently, it was interesting to evaluate the influence of excipients when the API is a high Raman scatterer. For that purpose, a multi-formulation model was developed and successfully validated with a RMSEP of 2.98% (w/w) in the best case. These two studies showed that thanks to the optimization of acquisition parameters, Raman handheld spectrophotometers methods were validated for two different case-study and could be applied to identify substandard medicines.

Using Near-infrared reflectance spectroscopy (NIRS) to predict glucobrassicin concentrations in cabbage and brussels sprout leaf tissue

BACKGROUND

Glucobrassicin (GBS) and its hydrolysis product indole-3-carbinol are important nutritional constituents implicated in cancer chemoprevention. Dietary consumption of vegetables sources of GBS, such as cabbage and Brussels sprouts, is linked to tumor suppression, carcinogen excretion, and cancer-risk reduction. High-performance liquid-chromatography (HPLC) is the current standard GBS identification method, and quantification is based on UV-light absorption in comparison to known standards or via mass spectrometry. These analytical techniques require expensive equipment, trained laboratory personnel, hazardous chemicals, and they are labor intensive. A rapid, nondestructive, inexpensive quantification method is needed to accelerate the adoption of GBS-enhancing production systems. Such an analytical method would allow producers to quantify the quality of their products and give plant breeders a high-throughput phenotyping tool to increase the scale of their breeding programs for high GBS-accumulating varieties. Near-infrared reflectance spectroscopy (NIRS) paired with partial least squares regression (PLSR) could be a useful tool to develop such a method.

RESULTS

Here we demonstrate that GBS concentrations of freeze-dried tissue from a wide variety of cabbage and Brussels sprouts can be predicted using partial least squares regression from NIRS data generated from wavelengths between 950 and 1650 nm. Cross-validation models had R² = 0.75 with RPD = 2.3 for predicting µmol GBS·100 g^-1 fresh weight and R² = 0.80 with RPD = 2.4 for predicting µmol GBS·g^-1 dry weight. Inspections of equation loadings suggest the molecular associations used in modeling may be due to first overtones from O-H stretching and/or N-H stretching of amines.

CONCLUSIONS

A calibration model suitable for screening GBS concentration of freeze-dried leaf tissue using NIRS-generated data paired with PLSR can be created for cabbage and Brussels sprouts. Optimal NIRS wavelength ranges for calibration remain an open question.

Prediction of Agro-Morphological and Nutritional Traits in Ethiopian Mustard Leaves (Brassica Carinata A. Braun) by Visible-Near-Infrared Spectroscopy

The particular characteristics of some of the Ethiopian mustard accessions available from seed banks could be used to increase the production and the diversity of products available to consumers and to improve their general quality. The objectives of this study were to determine the genetic variability for agro-morphological (days to first flowering: DFF and leaf pubescence: LP) and nutritional traits (total phenolic content: TPC) among accessions, and to evaluate the potential of near-infrared spectroscopy (NIRS) to predict these traits in Ethiopian mustard leaves. A great variation was found for the traits evaluated. The reference values were regressed against different spectral transformations by modified partial least-squares (MPLS) regression. The coefficients of determination in cross-validation (R²cv) shown by the equations for DFF, LP and TPC were 0.95, 0.63 and 0.99, respectively. The standard deviation to standard error of cross-validation ratio (RPD), were for these traits, as follows: DFF: 4.52, LP: 1.53 and, TPC: 24.50. These results show that the equations developed for DFF and TPC in Ethiopian mustard, can be predicted with sufficient accuracy for screening purposes and quality control, respectively. In addition, the LP equation can be used to identify those samples with "low", "medium" and "high" groups. From the study of the mean and deviation standard spectra, and regression vectors of MPLS models it can be concluded that some major cell components, highly participated in modelling the equations for these traits.

Agronomic performance of new open pollinated experimental lines of broccoli (Brassica oleracea L. var. italica) evaluated under organic farming

In order to develop new open pollinating cultivars of broccoli for organic farming, two experiments were conducted during fall 2015 and spring 2016. This study was aimed at comparing the agronomic performance of eleven new open pollinating breeding lines of broccoli to introduce new lines and to test their seasonal suitability for organic farming. Field experiments were carried out at the organic research station Kleinhohenheim of the University of Hohenheim (Stuttgart-Germany). Different agronomic traits total biomass fresh weight, head fresh weight, head diameter, hollow-stem, fresh weight harvest index and marketable yield were assessed together with commercial control cultivars. The data from both experiments were analyzed using a two-stage mixed model approach. In our study, genotype, growing season and their interaction had significant effects on most traits. Plants belonging to the fall growing season had bigger sizes in comparison to spring with significantly (p< 0.0001) higher biomass fresh weight. Some experimental lines had significant lower head fresh weight in spring in comparison to the fall season. The high temperature during the harvest period for the spring season affected the yield negatively through decreasing the firmness of broccoli heads. The low average minimum temperatures during the spring growing season lead to low biomass fresh weight but high fresh weight harvest index. Testing the seasonal suitability of all open pollinating lines showed that the considered fall season was better for broccoli production. However, the change in yield between the fall and the spring growing season was not significant for "Line 701" and "CHE-MIC". Considering the expression of different agronomic traits, "CHE-GRE-G", "Calinaro" and "CAN-SPB" performed the best in the fall growing season, and "CHE-GRE-G", "CHE-GRE-A", "CHE-BAL-A" and "CHE-MIC" and "Line 701" were best in the spring growing season, specifically due to the highest marketable yield and proportion of marketable heads.